Autonomous intelligent cruise control using a novel multiple-controller framework incorporating fuzzy-logic-based switching and tuning

This paper presents a novel intelligent multiple-controller framework incorporating a fuzzy-logic-based switching and tuning supervisor along with a generalised learning model (GLM) for an autonomous cruise control application. The proposed methodology combines the benefits of a conventional proportional-integral-derivative (PID) controller, and a PID structure-based (simultaneous) zero and pole placement controller. The switching decision between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using a fuzzy-logic-based supervisor, operating at the highest level of the system. The supervisor is also employed to adaptively tune the parameters of the multiple controllers in order to achieve the desired closed-loop system performance. The intelligent multiple-controller framework is applied to the autonomous cruise control problem in order to maintain a desired vehicle speed by controlling the throttle plate angle in an electronic throttle control (ETC) system. Sample simulation results using a validated nonlinear vehicle model are used to demonstrate the effectiveness of the multiple-controller with respect to adaptively tracking the desired vehicle speed changes and achieving the desired speed of response, whilst penalising excessive control action. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Suprathermal electron flux peaks at stream interfaces: Signature of solar wind dynamics or tracer for open magnetic flux transport on the Sun?

The high variability of the intensity of suprathermal electron flux in the solar wind is usually ascribed to the high variability of sources on the Sun. Here we demonstrate that a substantial amount of the variability arises from peaks in stream interaction regions, where fast wind runs into slow wind and creates a pressure ridge at the interface. Superposed epoch analysis centered on stream interfaces in 26 interaction regions previously identified in Wind data reveal a twofold increase in 250 eV flux (integrated over pitch angle). Whether the peaks result from the compression there or are solar signatures of the coronal hole boundary, to which interfaces may map, is an open question. Suggestive of the latter, some cases show a displacement between the electron and magnetic field peaks at the interface. Since solar information is transmitted to 1 AU much more quickly by suprathermal electrons compared to convected plasma signatures, the displacement may imply a shift in the coronal hole boundary through transport of open magnetic flux via interchange reconnection. If so, however, the fact that displacements occur in both directions and that the electron and field peaks in the superposed epoch analysis are nearly coincident indicate that any systematic transport expected from differential solar rotation is overwhelmed by a random pattern, possibly owing to transport across a ragged coronal hole boundary.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Weighted input predictive controller

A discrete-time algorithm is presented which is based on a predictive control scheme in the form of dynamic matrix control. A set of control inputs are calculated and made available at each time instant, the actual input applied being a weighted summation of the inputs within the set. The algorithm is directly applicable in a self-tuning format and is therefore suitable for slowly time-varying systems in a noisy environment.

Adaptive general predictive controller for nonlinear systems

A nonlinear general predictive controller (NLGPC) is described which is based on the use of a Hammerstein model within a recursive control algorithm. A key contribution of the paper is the use of a novel, one-step simple root solving procedure for the Hammerstein model, this being a fundamental part of the overall tuning algorithm. A comparison is made between NLGPC and nonlinear deadbeat control (NLDBC) using the same one-step nonlinear components, in order to investigate NLGPC advantages and disadvantages.

Spectacle/gender/history: the case of Gone with the Wind

The essay asserts that, since pioneering work in the 1970s and 80s (in Screen in particular), the study of classical Hollywood cinema has failed adequately to acknowledge and understand the role of spectacle therein. This essay outlines theoretical but, even more, practical understandings of particular kinds of spectacle; they are susceptible to the practice of close analysis. Seeking to discuss spectacle in precise terms and in particular contexts, I define two kinds of spectacle associated with the historical film: ‘the decor of history’ and ‘the spectacular vista’. The example of Gone with the Wind illustrates the interrelationship between these two kinds of spectacle and their associations with particular ideas of femininity and masculinity. This gendering of spectacle is related to ‘the historical gaze’, a performative gesture that exemplifies the wider rhetoric of historical films, in their seeking to address the historical knowledge of the film spectator and to uphold a vision of history as being driven by powerful men, aware of their own destiny. Over the course of the three famous hilltop scenes in Gone with the Wind, one can plot Scarlett O'Hara's increased access to this kind of foresight and fortitude coded as ‘masculine’. This character arc can also be traced through Scarlett's shifting place within the film's use of spectacle: she begins the film wholly preoccupied with the domestic world of lavish parties and beautiful gowns; however, after her encounter with cataclysmic history visualized as a vast, terrible spectacle (the fall of Atlanta), Scarlett assumes the role occupied by her broken and emasculated father.

Magnetic discontinuities in the near-earth solar wind: evidence of in-transit turbulence or remnants of coronal structure?

Fluctuations in the solar wind plasma and magnetic field are well described by the sum of two power law distributions. It has been postulated that these distributions are the result of two independent processes: turbulence, which contributes mainly to the smaller fluctuations, and crossing the boundaries of flux tubes of coronal origin, which dominates the larger variations. In this study we explore the correspondence between changes in the magnetic field with changes in other solar wind properties. Changes in density and temperature may result from either turbulence or coronal structures, whereas changes in composition, such as the alpha-to-proton ratio are unlikely to arise from in-transit effects. Observations spanning the entire ACE dataset are compared with a null hypothesis of no correlation between magnetic field discontinuities and changes in other solar wind parameters. Evidence for coronal structuring is weaker than for in-transit turbulence, with only ∼ 25% of large magnetic field discontinuities associated with a significant change in the alpha-to-proton ratio, compared to ∼ 40% for significant density and temperature changes. However, note that a lack of detectable alpha-to-proton signature is not sufficient to discount a structure as having a solar origin.

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.